6136 6146 update the default writer flag

monai/data/image_writer.py

@@ -373,11 +373,11 @@ class ITKWriter(ImageWriter):
     output_dtype: DtypeLike = None
     channel_dim: int | None
 
-    def __init__(self, output_dtype: DtypeLike = np.float32, affine_lps_to_ras: bool = True, **kwargs):
+    def __init__(self, output_dtype: DtypeLike = np.float32, affine_lps_to_ras: bool | None = None, **kwargs):
         """
         Args:
             output_dtype: output data type.
-            affine_lps_to_ras: whether to convert the affine matrix from "LPS" to "RAS". Defaults to ``True``.
+            affine_lps_to_ras: whether to convert the affine matrix from "LPS" to "RAS". Defaults to ``None``.
                 Set to ``True`` to be consistent with ``NibabelWriter``,
                 otherwise the affine matrix is assumed already in the ITK convention.
             kwargs: keyword arguments passed to ``ImageWriter``.
@@ -478,7 +478,7 @@ def create_backend_obj(
         channel_dim: int | None = 0,
         affine: NdarrayOrTensor | None = None,
         dtype: DtypeLike = np.float32,
-        affine_lps_to_ras: bool = True,
+        affine_lps_to_ras: bool | None = None,
         **kwargs,
     ):
         """
@@ -489,16 +489,21 @@ def create_backend_obj(
             channel_dim: channel dimension of the data array. This is used to create a Vector Image if it is not ``None``.
             affine: affine matrix of the data array. This is used to compute `spacing`, `direction` and `origin`.
             dtype: output data type.
-            affine_lps_to_ras: whether to convert the affine matrix from "LPS" to "RAS". Defaults to ``True``.
+            affine_lps_to_ras: whether to convert the affine matrix from "LPS" to "RAS". Defaults to ``None``.
                 Set to ``True`` to be consistent with ``NibabelWriter``,
                 otherwise the affine matrix is assumed already in the ITK convention.
             kwargs: keyword arguments. Current `itk.GetImageFromArray` will read ``ttype`` from this dictionary.
 
         see also:
 
             - https://github.com/InsightSoftwareConsortium/ITK/blob/v5.2.1/Wrapping/Generators/Python/itk/support/extras.py#L389
+
         """
-        if isinstance(data_array, MetaTensor) and data_array.meta.get(MetaKeys.SPACE, SpaceKeys.LPS) != SpaceKeys.LPS:
+        if (
+            isinstance(data_array, MetaTensor)
+            and affine_lps_to_ras is None
+            and data_array.meta.get(MetaKeys.SPACE, SpaceKeys.LPS) != SpaceKeys.LPS
+        ):
             affine_lps_to_ras = False  # do the converting from LPS to RAS only if the space type is currently LPS.
         data_array = super().create_backend_obj(data_array)
         _is_vec = channel_dim is not None

monai/transforms/io/array.py

@@ -440,6 +440,7 @@ def set_options(self, init_kwargs=None, data_kwargs=None, meta_kwargs=None, writ
             self.meta_kwargs.update(meta_kwargs)
         if write_kwargs is not None:
             self.write_kwargs.update(write_kwargs)
+        return self
 
     def __call__(self, img: torch.Tensor | np.ndarray, meta_data: dict | None = None):
         """

monai/transforms/io/dictionary.py

@@ -296,6 +296,7 @@ def __init__(
 
     def set_options(self, init_kwargs=None, data_kwargs=None, meta_kwargs=None, write_kwargs=None):
         self.saver.set_options(init_kwargs, data_kwargs, meta_kwargs, write_kwargs)
+        return self
 
     def __call__(self, data):
         d = dict(data)

tests/test_image_rw.py

@@ -167,7 +167,7 @@ def nrrd_rw(self, test_data, reader, writer, dtype, resample=True):
             filepath = f"testfile_{ndim}d"
             saver = SaveImage(
                 output_dir=self.test_dir, output_ext=output_ext, resample=resample, separate_folder=False, writer=writer
-            )
+            ).set_options(init_kwargs={"affine_lps_to_ras": True})
             test_data = MetaTensor(
                 p(test_data), meta={"filename_or_obj": f"{filepath}{output_ext}", "spatial_shape": test_data.shape}
             )